Traditionally, the rural spaces refer to places where natural
landscapes and low population density predominate (OGDUL, 2010). The
rural definitions, in academic institutions and others, have generated
in the last decades a wide discussion without a consensus definition.
The UNITED NATIONS (2009) alerted that 2007 was the first time in human
history when the majority of the world's people were living in
urban area. WIMBERLEY et al. (2007) also calculated May 23, 2007 as the
day when this finally happened. These assertions are questionable
because there is not a worldwide rural definition and these estimations
use the official definitions of the countries that belong to these
institutions (MINNESOTA POPULATION CENTER, 2013).

The lack of consensus on rural meaning leads to other dilemmas. The
underestimates or overestimates of poverty in rural communities are a
consequence of the indefiniteness. Suppose that country X (Malaysia)
considers urban spaces as 1,000 or more persons and country Y (Senegal)
deliberates this number against 10,000 inhabitants (MINNESOTA POPULATION
CENTER, 2013). Even if the distribution of the population among
different sized places within X and Y is supposedly identical, a
considerable higher proportion of the inhabitants, and of the people
under the poverty line of, for example, one U.S. dollar per day, would
be counted as rural in Y but not in X (INTERNATIONAL FUND FOR
AGRICULTURAL DEVELOPMENT, 2001).

Most international organizations have been concerned with rural
definitions. The OECD (2009) has created a typology to define rural
spaces. This method of delimiting the rural spaces has been a tool for
many public policies. Considering this scenario, this study aimed to
apply the OECD typology in Brazil. The country was chosen by its
territorial-economic dimensions and by the lack of a unified rural
definition. Other attempts to apply this methodology in Brazil, such as
VEIGA (2004) and the OECD report (2013a), had limitations in the
databases used. Both research projects utilized only data aggregated by
municipality that can produce inaccuracies in the results.

The present study is structured in three sections. The first one is
concerned with an introduction and the literature review recounts
several approaches for determining the rural definition since the 1970s
and the OECD rural-urban definition. Subsequently, the methodology and
the results are displayed and finally the conclusions are presented. The
study aims to set an operational definition of the rural spaces and to
use an operational methodology to implement it in Brazil. In addition,
this paper purposes to compare the results with others literature
attempts, namely VEIGA (2004) and the OECD report (2013a).

Until the present, it was not possible to have a consensus on the
definition of the rural spaces. BENGS & SCHMIDT-THOME (2006)
synthetized the approaches for understanding the rural and urban spaces.
The authors have created four groups for rural definitions: implicit
definitions; statistically derived policy-relevant differentiation of
rural areas; statistically derived index of rurality; and neutrally
defined rural delimitation.

Part of the published researches defines the rural space using
intuitive ideas, theories or empirical evidences. The rural point of
view does not consider the use of statistical tests to consolidate the
results. OECD definition might be included in this approach. The OECD
three-fold classifications, which are discussed in the next section,
consider only population density and the size of urban centers for
defining the delimitation of rural spaces.

The second approach according to BENGS & SCHMIDT-THOME (2006)
is a "statistically derived policy-relevant differentiation"
of rural areas. This approach commonly classifies rural by means of an
exploratory study utilizing statistics tools. The variable selection is
predefined by theoretical criteria. Authors such as MALINEN (1995) are
including in this research line.

Also following BENGS & SCHMIDT-THOME (2006) in statistical
approaches for rural definition, one must consider the rurality index
method. This point of view has as its central mentor CLOKE (1977; 1992),
who started this type of study in 1977, and used several ways for
calculating the rurality indexes. OGDUL (2010) identified a trend in
this type of research using the definition of rurality as a mode of
life. This approach has a limited connection inter-authors, like as
demonstrate by BRAGA et al. (2014). They used the social network
methodology and concluded that this research line has low modularity and
low density among authors quote.

The last line of research pointed out by BENGS & SCHMIDT-THOME
(2006) is called "neutrally defined rural" delimitation. This
approach is mostly used as a preliminary stage in a most complex
analysis. The first step of the OECD rural-urban typology (1994) is in
this approach. Determination the rural areas will be explored more in
the next.

The OECD regional typology was published the first time in 1994
(OECD, 1994). This typology was reaffirmed in later OECD reports (2009,
2013a, 2013b, 2013c). It follows three separate steps. In the first
step, it recognizes rural communities according to population density.
The community is considered urban if its population density is over 150
inhabitants per square kilometer. The exceptions are Japan and Korea,
which consider urban population density as over 500 inhabitants per
square kilometer. These exceptions are used when the national population
density exceeds 300 inhabitants per square kilometer in the last
demographic census (OECD, 2013c).

Step two consists in aggregating this data in Territorial Level 3
(TL3) and categorizing it as "Predominantly Urban",
"Intermediate" and "Predominantly Rural". The
percentage of the population living in rural areas is used for determine
the TL3 regions as: Predominantly Urban (PU), if less than 15% of the
population is living in rural areas; Intermediate (IN), if the
percentage of the population living in rural areas is between 15% and
50%; Predominantly Rural (PR), if more than 50% of the population is
living in rural areas (OECD, 2013c).

Finally, the urban centers inside the TL3 regions can change the
previously classification. If a region is classified as Predominantly
Rural and contains an urban center with more than 200,000 inhabitants
(500,000 for Japan and Korea) and this represents at least 25% of
population, its region becomes Intermediate. If a region is set as
Intermediate and contains an urban center with more than 500,000
inhabitants (1,000,000 for Japan and Korea) and this represents at least
25% of the population, its region becomes Predominantly Urban (OECD,
2013c).

MATERIALS AND METHODS

This study used the data from the 2010 Brazilian demographic
census, utilizing the most disaggregate data available from the
Instituto Brasileiro de Geografia e Estatistica (IBGE), census sectors.
Besides IBGE data, the study also used digital maps for all 314,018
census sectors to measure the areas and display results with the help of
the Terrawiew software version 4.2.2 (IBGE, 2013a, 2013b).

This research used a different method from the OECD report (2013a)
and VEIGA (2004) because it utilized census sector data. The most
disaggregate data was justified once the OECD typology determined, in
its first step, the use of data from "local units" or
communities (OECD, 2010, 2013a, 2013b, 2013c). In addition, the use of
census sector data contributed to better homogeneity of the sample, once
this data is aggregated by the quantitative dwellings.

For Brazil, OECD determines the TL3 regions as the mesorregioes
(subdivisions) of IBGE. Thus, Brazil was divided in one hundred
thirty-seven TL3 regions (OECD, 2013a). In this way, each TL3 regions in
Brazil receives the classification of "predominantly urban",
"intermediate" or "predominantly rural", following
the three steps proposed in the OECD typology.

In the present study, population density was first calculated in
each census sector, which was classified as a rural or urban area.
Census sector was a rural area if it had less than 150 inhabitants per
square kilometer. The results were combined in TL3 regions and the
percentage of rural population was obtained. If the percentage was more
than 50%, the TL3 region was Predominantly Rural; if the percentage was
between 15% and 50%, the TL3 region was Intermediate; and if the
percentage was less than 15%, the TL3 regions was Predominantly Urban.

Finally, if a TL3 region was categorized as Predominantly Rural and
had a municipality with more than 200,000 inhabitants and this
municipality represented at least 25% of the population, the TL3 region
became Intermediate. If a TL3 region was fixed as Intermediate and
contained a municipality with more than 500,000 inhabitants and this
municipality represented at least 25% of the population, the TL3 region
became Predominantly Urban (OECD, 2013a).

RESULTS AND DISCUSSION

The results revealed Brazil as more urban than the OCDE report
(2013 a) and VEIGA (2004) estimations. Indeed, in this study, only one
TL3 region, Marajo in the State of Para, was classified as Predominantly
Rural (Figure 1). Brazil in 2010 had 87.48% of its population living in
urban areas, if considering the census sector as a local unit. That
percentage is higher than official statistics that present 84% of the
Brazilian population living in urban areas (IBGE, 2013b).

These results pointed to a high sensibility of OECD typology from
changes of aggregate level data. There are several differences between
the OECD report and results using census sectors data. Indeed, only ten
of one hundred thirty-seven areas maintained their classifications. OECD
report (2013a), as VEIGA (2004), uses data aggregated by municipalities
causing these differences.

Two regions were reclassified in the last proceedings, Centro-Norte
Piauiense (Central North Piaui) and Norte Maranhense (North Maranhao),
due to containing municipalities with over half a million inhabitants,
representing more than 25% of the TL3 region population, Teresina and
Sao Luiz respectively. This last proceeding was apparently ignored by
the OECD report. That report classified the Norte Maranhense (North
Maranhao) as Predominantly Rural. Others results of the OECD report can
be questioned; for example, the Metropolitana of Belo Horizonte
(Metropolitan Belo Horizonte) is set as an Intermediate region. However,
this region contains the third largest Brazilian state capital, the Belo
Horizonte municipality, with more than two million inhabitants.

Brazil is mostly composed by Intermediate TL3 regions, considering
the results of this research. The country has 86 Intermediate regions,
50 Predominantly Urban regions and just one Predominantly Rural region.
In the figure 1 it is possible to view the TL3 regions where the states
capitals are localized as Predominantly Urban, except in the States of
Acre, Rondonia and Tocantins, all in North Brazil.

The Brazilian TL3 regions are large as compared with TL3 regions of
the USA and Europe. Taking into account the OECD typology, they were
recalculated putting the municipalities in place of TL3 regions
(mesorregioes). Brazil had 5,565 municipalities in 2010, which have
ample heterogeneity of areas and population. The Brazilian
municipalities are the smallest level of political division. The OECD
typology on the level of municipality might be useful for public
policies. Figure 2 presents the OECD typology applied to Brazilian
municipalities.

The last proceeding of this classification was not used in this
analysis, because the municipality is the smallest level of Brazilian
official segmentation. Figure 2 presents a Brazil that is more rural
than TL3 region classifications. Four municipalities (Tupirama, Sao Joao
do Itaperiu, Cariri do Tocantins and Sao Felix do Tocantins) present
their populations as totally living in rural areas. Other municipalities
exhibit at least one census sector with more than 150 inhabitants per
square kilometer.

These results demonstrate a large gap with the VEIGA study (VEIGA,
2004, p.11) that stated 80% of Brazilian municipalities are rural. In
addition, VEIGA (2004) did not indicate which are these municipalities.
Instead, applying the OECD typology in Brazilian municipalities with
census sector data, 1,114 municipalities (20.6%) are Predominantly
Rural; 1,326 municipalities (23.8%) are Predominantly Urban; and 3,095
municipalities (55.6%) are Intermediate. Figure 2 demonstrates that a
large municipality, for example Altamira PA, can be Predominantly Urban
once its population is concentered.

CONCLUSION

Brazil does not have a national parameter to define the rural
areas. The rural areas are defined administratively by Brazilian
municipalities. However, official calculations of rural population,
despite their inaccuracies, have results close to the OECD typology
applied with census sectors data. Using the study methodology, Brazil is
not so much rural as pointed out by VEIGA (2004), official Brazilian
data (IBGE, 2014a), and OECD report (2013a).

Results presented that the OECD regional typology is highly
sensitive to changes in the level of aggregate data. Like other
approaches that consider rural as synonymous with low population
density, this bias is a limitation for similar studies. That sensibility
must be taken into account in transnational studies. Perhaps the best
way for making this type of study is creating comparable data areas,
like collecting the data in one square kilometer grid cells.

It is important to consider that any rural approach has its own
limitations. Understanding that there are other ways of measuring the
rural areas is essential. Futures studies can, for example, use the
rurality index approach. This approach can improve other rural views and
the results. This can be a promising way for attempting rural
determinations.

http://dx.doi.org/10.1590/0103-8478cr20150464

ACKNOWLEDGEMENTS

This project was supported by the Universidade Federal de Vicosa
(Brazil), Universidade do Minho (Portugal), and Conselho Nacional de
Desevolvimento Cientifico e Tecnologico (CNPq) grant number
PDSE-14079/13-5.